The widespread availability of optical image capture devices, such as cameras, implemented on, or with, computing systems, such as mobile devices and smart phones, has resulted in a significant number applications and systems that rely on the ability to extract data from images of hard copy documents in order to obtain various types of information.
For instance, many currently available financial management systems, financial transaction management systems, tax-preparation systems, and various other data management systems, obtain data from optical images of source documents processed using Optical Character Recognition (OCR) systems, or similar data extraction technologies.
While the use of optical images and data extraction technology provides some capability to obtain information with minimal user input, there are several issues associated with these methods. One long-standing problem associated with using optical images and data extraction technology to obtain data is that the optical image of the source document must be of sufficient quality to allow the data extraction technology to identify and extract the individual characters and symbols represented in the optical image of the source document.
The problem arises because many source documents are of significant size and, therefore, in order to capture an optical image of the entire source document, the camera, or other optical image capture device, must be positioned a relatively significant distance away from the source document; often 10 inches or more away from the source document. As a result, when the camera, or other optical image capture device, automatically adjusts various image capture parameters, such as the focus and exposure settings, to capture the source document image, those settings are only optimized, and effective, for a portion of the source document, such as the middle portion of the source document. However, the data desired from the source document is often distributed throughout the source document, including in the portions of the source document where the image capture parameters, such as the focus and exposure, are not optimized.
As a result, currently, significant portions of the source document information are often not clearly captured in the source document image and therefore cannot be identified and processed using OCR, or other data extraction technologies. Consequently, in many cases, an individual attempting to provide an optical image of a source document is often forced to capture multiple images of the source document, and provide the multiple images of the source document to the data extraction technology before the source document data is obtained. This is a frustrating and time-consuming process for the user and often largely negates the potential advantages of using optical images and data extraction technology.
In addition, the process of transferring multiple images of the source document to a data extraction technology engine, such as an OCR capability implemented by a remote computing system/server, creates several problems in and of itself. For instance, the bandwidth required to transfer multiple images of the source document to a data extraction engine server is significant, and often an issue. In addition, the processor cycles required to transfer and process multiple images of the source document is also often problematic. Finally, the transfer of multiple images of the source document can represent a security risk. This is particularly problematic when the source document is a sensitive document such as a bill, invoice, tax document, etc.
What is needed is a method system for accurately, efficiently, and reliably providing an optimized optical image of a source document to a data extraction engine, such as OCR capability, without requiring the transfer of multiple images of the source document or requiring significant user input or action.